Estimation of On‐Road PM <sub>2.5</sub> Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning-Reference-Cited by-同舟云学术

Estimation of On‐Road PM _2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

Published:2022-09 Issue:9 Volume:6 Page:
ISSN:2471-1403
Container-title:GeoHealth
language:en
Short-container-title:GeoHealth

Author:

Tong Chengzhuo¹^ORCID,Shi Zhicheng²^ORCID,Shi Wenzhong¹^ORCID,Zhang Anshu¹

Affiliation:

1. Otto Poon Charitable Foundation Smart Cities Research Institute and Department of Land Surveying and Geo‐Informatics The Hong Kong Polytechnic University Hong Kong China

2. Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China

Publisher

American Geophysical Union (AGU)

Subject

Health, Toxicology and Mutagenesis,Management, Monitoring, Policy and Law,Public Health, Environmental and Occupational Health,Pollution,Waste Management and Disposal,Water Science and Technology,Epidemiology,Global and Planetary Change

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022GH000669

Reference59 articles.

1. A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

2. Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe – The ESCAPE project

3. Contribution of AOD-PM2.5 surfaces to respiratory-cardiovascular hospital events in urban and rural areas in Baltimore, Maryland, USA: New analytical method correctly identified true positive cases and true negative controls

4. A landscape scale study in Southeast China investigating the effects of varied green space types on atmospheric PM2.5 in mid-winter

5. Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach

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